Abstract #300152

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JSM 2003 Abstract #300152
Activity Number: 209
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #300152
Title: Are Our Data Symmetric?
Author(s): Sumithra J. Mandrekar*+ and Jayawant N. Mandrekar
Companies: Mayo Clinic and The Ohio State University
Address: 3625, 41st Street NW, Apt. 120, Rochester, MN, 55901,
Keywords: nonparametric ; skewness ; symmetry
Abstract:

Skewness indicates a lack of symmetry in a distribution. Knowing the symmetry of the underlying data is essential for parametric analysis, fitting distributions, or doing transformations to the data. The coefficient of skewness is the commonly used measure to identify a lack of symmetry in the underlying data, although graphical procedures can also be effective. We discuss three different methods to assess skewness: traditional coefficient of skewness index, skewness index based on the L-moments discussed by Hosking (1990), and the asymptotic test of symmetry developed by Randles, et al. (1980). With this work, we provide easy to implement S-PLUS® functions as well as discuss the advantages and shortcomings of each technique.

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